Database for pre-screening potentially litigious patients

ABSTRACT

Modern medical practitioners face a real risk from frivolous lawsuits initiated by overly litigious patients. Even if innocent of any malpractice, a doctor subject to such lawsuits may experience personal stress and extended periods of time diverted from practice in addition to greatly increased insurance premiums. This patent describes a database system that allows medical professionals to gauge the legal risk presented by new patients, giving them the opportunity to avoid medical involvement with those individuals most prone to engaging in unwarranted legal actions. Other applications of the present system pertain to insurance companies, legal services and other professional service providers.

[0001] (Conversion of Provisional Application No. 60/307,561 to autility application)

CROSS REFERENCE TO RELATED APPLICATIONS

[0002] U.S. Patent Documents

[0003] U.S. Pat. No. 5,325,291 June, 1994 Garrett et al. 705/1.

[0004] U.S. Pat. No. 5,752,237 May, 1998 Cherny 705/4.

[0005] U.S. Pat. No. 5,852,808 December, 1998 Cherny 705/4

[0006] U.S. Pat. No. 5,875,431 December, 1998 Heckman et al. 705/7.

[0007] U.S. Pat. No. 5,895,450 April, 1999 Sloo 705/1.

[0008] Foreign Patent Documents

[0009] WO 9740460 October, 1997 WO.

REFERENCE TO SEQUENCE LISTING, A TABLE

[0010] Title of the Invention—page 1

[0011] Inventors and Addresses—page 1

[0012] Conversion of Provisional Application No. 60/307,561—page 1

[0013] Cross Reference to Related Applications—page 1

[0014] Background of the Invention—page 2

[0015] Brief Summary of the Invention—page 2

[0016] Description of the drawings—page 3

[0017] Description of the Invention—page 3

[0018] Abstract—on a separate sheet of paper

[0019] Claims—on separate sheets

BACKGROUND OF THE INVENTION

[0020] The need for modalities to curb the spiraling costs ofprofessional services, which is driven in large part by expense relatedto legal costs and the cost of insurance protection against law suits,is widely recognized. This problem is disproportionately severe in therealm of medico-legal issues and is a major problem for virtually allproviders of professional services and in the service industry, ingeneral. In many cases, physicians are relocating, retiring or changingprofession. Hospitals are curbing services at the cost of decliningquality of care or are closing their doors, in many cases after over onehundred years of community care. Legal defense and extremely highsettlements have created insurmountable debts. Similar high cost ofclient-initiated law suits are impacting virtually all professions. Thusthe need to avoid litigious clients and situations is obvious andidentification of multiple client and situational factors by a systemwhich enables professional service providers to pre-screen and identifyclients who have a greater than average potential for initiating lawsuits is important in order to minimize the ultimate risk of litigationagainst the physician as well as other professionals.

BRIEF SUMMARY OF THE INVENTION

[0021] This patent describes a database system that allows medical andother professionals to gauge the legal risk presented by newpatients/clients, giving them the opportunity to avoid medicalinvolvement with those individuals most prone to engaging in unwarrantedlegal actions. In this way, such efficient knowledge disseminationultimately provides the physician with means for avoiding or reducingthe risks of liability litigation through patient motivated medicalmalpractice suits before the fact by enabling him/her to make much moreintelligently informed decisions regarding such questions as acceptanceof that patient or conversely, denial of the associated needed medicalservices to that given patient (or acceptance for particular types ofmedical services or treatments) as well as to what degree is specialmedical attention and/or personalized care directed to the emotionalneeds of the patient most significantly warranted in order to minimizethe ultimate risk of litigation against the physician eventuallyresulting from that patient. In formation within this system will allowimproved physician-patient matching. Other applications of the presentsystem pertain to hospitals, insurance companies, legal services andother professional service providers. For example, using the informationby the present novel system will enable insurance carriers to moreappropriately pro rate individual premiums based upon more accurateevaluation of risk profile.

BRIEF DESCRIPTION OF THE DRAWINGS

[0022]FIG. 1 illustrates the use of the litigious patient screeningsystem. First, a user transmits information about the identity of apotential patient either manually (through a web interface) orautomatically (through patient management software). This information isthen fed through a system that (1) matches the patient to a database(linking the individual to other doctors, past lawsuits, relatedlawyers, etc.), and (2) uses a statistical model to predict thelikelihood of litigation and expected cost any such lawsuits. This riskassessment is then transmitted back to the user, and is either displayedon a web page or entered automatically into the office system, dependingon the mode of initiation.

DETAILED DESCRIPTION OF THE INVENTION

[0023] 1. Problem

[0024] Many of the physicians practicing in urban or suburban areas(representing perhaps 50% or more of the total population in the US),and particularly those practicing in urban areas in the northeasternUnited States, have a high probability of facing egregious medicalmalpractice suits. Whereas an estimated 95% of patients are essentiallynon-litigious, with regards to physician medico-legal liability issues,it is felt that a mechanism to identify the small percentage of thosepatients who are litigious is desperately needed. Certain specialtiesare especially predisposed to medical malpractice claims. Some of themost vulnerable include obstetrics, neurosurgery, vascular surgery andpediatrics, although there is an increasing incidence of lawsuits acrossall surgical specialties. In many cases physicians are leaving thepractice of medicine or relocating to avoid geographic areas with higherthan average rates of medico-legal action and unreasonably high damageawards. Hospitals and other medical establishments spend large amountsof money and personnel effort in defensive countermeasures, sincefrivolous lawsuits affect their ability to properly subsidize thedelivery of quality health care, as well as their ability to locate newdoctors locally. These factors are a major cause of the spiraling out ofcontrol costs of medical care, which directly impacts government,industry and finally economic well being. Parallel problem situationsare impacting paramedical services, non-medical professional providers,insurance carriers, and even the legal service providers themselves. Andthe same type of system as described in this invention can be used inparallel to the system described in preemptive measures to avoid thelitigious client or situation in non-medical applications.

[0025] 2. Proposed Solution

[0026] The present service substantially addresses this major problem byenabling physicians to pre-screen potential patients for greater thanaverage litigiousness. The system consists of a computer database,accessed either on a per-use basis or as an add-on to standard practicemanagement software, computerized patient registration systems, intowhich the medical professional enters the patient's name, address, andsocial security number and other demographic data. The system uses astored history of medical lawsuits (among other data) in combinationwith statistical algorithms to generate a score. Much like a the creditscores used by loan officers to gauge an individual's likelihood ofdefault, the score generated by this system gives the medicalprofessional a quantitative basis for assessing the risk that a givenpatient will engage in frivolous litigation. If the risk is too high forthe professional's preference, he/she can choose to not establish amedical relationship with the patient, which is the practitioner's legalright. The present system is similarly applied to non-medical serviceproviders using the above array of data to determine the client orsituation with the greatest potential for lawsuits.

[0027] 3. Database Organization

[0028] The creation of the relational database supporting the patienttracking system would be complex, in that many different sources oflegal data would need to be compiled; however, the technical aspects ofthe database itself would be quite straightforward. It would simplycontain records on the identities of patients, doctors, expertwitnesses, lawyers, and judges. Each record would contain various formsof medical, legal, and demographic information, as well as links toother patients, doctors, expert witnesses, lawyers, and judges.

[0029] In particular:

[0030] Patient records would include:

[0031] Links to family members

[0032] Medical history (including health status and doctors previouslyseen)

[0033] Socioeconomic status

[0034] Demographic information (including age)

[0035] Record of the nature of previous disease (by standard codenumber) processes and the timing of the disease(s)

[0036] Current disease(s)

[0037] Family history of disease(s) and proximity of blood relationshipto patient

[0038] Nature of disease (litigious disease process) for which definitedegrees and medical malpractice cannot be proven or disprovenobjectively and conclusively (e.g., back pain, thoracic outlet syndrome,certain neuropathies, emotional trauma such as that associated withsuffering, intractable pain syndromes).

[0039] Evidence of instability such as mental records, criminalbackground, evidence of previous courses of medical treatment notfollowed (checking out of hospitals by signing out against medicaladvice, not following prescription plans, present and historicalsubjective level of fear of receiving treatment, in general, or of thepresent condition, etc.)

[0040] Previous litigation history ((including medico-legal andnon-medico-legal as well as suits initiated by the individual and thosebrought against the individual by a third party (e.g., were the suits ofa medico-legal nature, were the suits egregious or most likelyunjustified such as summary judgments in favor of the defendant).

[0041] number of suits (total)

[0042] number of suits of a medico-legal nature

[0043] types of suits

[0044] doctors, lawyers, and expert witnesses involved

[0045] money demanded

[0046] suit outcomes

[0047] Does the patient have a history of initiating suits, which areeventually dismissed or consist of frivolous lawsuits?

[0048] The patient's history of initiating (or his/her immediate family)medico-legal suits (such as number of suits initiated and awards orsettlements recovered).

[0049] Does the patient have a history or suspected history of feigninginjuries or illnesses?

[0050] Does the patient have a history or suspected history ofcommitting medical or disability insurance fraud?

[0051] Doctor records of referring physicians (typically belonging toother doctors) would include:

[0052] Educational/professional profile

[0053] Patients seen

[0054] Commendations or condemnations by medical boards andorganizations (including hospital review boards, state medicalorganizations).

[0055] Physician ratings services

[0056] Number of malpractice cases already faced, with outcomes andamounts.

[0057] Demographic information

[0058] Lawyer records would include:

[0059] Educational/professional profile

[0060] Commendations or condemnations by legal boards and organizations

[0061] Lawyer ratings services

[0062] Number of cases won/lost/dismissed

[0063] Aggressiveness of solicitation (does lawyer “chase ambulances” oronly take on valid cases?)

[0064] Does lawyer have a history of initiating lawsuits which areeventually dismissed or consist of frivolous lawsuits?).

[0065] If so, what is the lawyer's history of success in this regard?

[0066] Demographic information

[0067] Involvement with patients, doctors, and judges

[0068] Degree of public notoriety (extracted from on-line media)

[0069] Judge records would include:

[0070] History of cases seen

[0071] Commendations or condemnations by review boards

[0072] Degree of public notoriety (extracted from on-line media)

[0073] Expert witness records would include:

[0074] Educational/professional profile

[0075] Demographic information

[0076] Case involvement

[0077] Overall success

[0078] Degree of public notoriety (extracted from on-line media)

[0079] 4. Implementation and Algorithms

[0080] Simply put, the function of this system is to receive as inputidentifying information about a patient (e.g. name, address, socialsecurity number), and to return a value representing the predictedlitigiousness of the given patient, such as the probability of a lawsuitas a result of treating the present condition as well as predicteddollar amounts of any ensuing lawsuits and a breakdown which correlatespredicted probability with ultimate monetary recovery by the plaintiff:

[0081] a. In general as an overall probability statistic,

[0082] b. If litigation were to ensue:

[0083] The system could also reveal the effect that such a law suitwould have on the physician's insurance premiums, and if these premiumsare adjusted in accordance with the physician's adherence to avoidingcertain levels of litigation risk via the present system, what would bethe direct consequences on the physician's insurance premiums for:

[0084] a. Accepting the present patient and,

[0085] b. Accepting other patients within the same approximate risklevel of the present patient based upon the litigious risk statistics ofthe physician's other patients. The system could even provide a breakdown of what the direct monetary losses would be in this regard foraccepting the patient compared with the likely direct monetary gainsthat the physician would achieve for accepting the patient for his/herpresent condition as well as analogously what the comparative long termeffects would be on direct income from accepting other patients at asimilar risk level compared to the anticipated losses sustained as aresult of insurance premium increases resulting from accepting thissimilar higher risk segment of the physician's current typicalpopulation of patient candidates, and this value could also be adjustedin the event that litigation did occur in accordance with

[0086] a. The estimated associated probability thereof as at the averagepredicted plaintiff recovery under the present conditions,

[0087] b. The predicted probability/plaintiff recovery distributionbased upon all of the relevant variables of the present type ofcircumstances (e.g., likely patient condition, general health,litigiousness factors, etc.).

[0088] In the preferred embodiment of the system, the service is bundledwith a practice management system, which maintains persistentconnections to a central database of medico-legal information. Inparticular, when the receptionist in the physician's office, clinic orhospital (directly or over the phone) enters patient information after apatient signs in or schedules an advanced appointment, the systemautomatically queries the database remotely and instantaneously deliversthe litigation risk profile. Examples of such practice managementsystems include WebMd (ww.webmd.com), CitX's IntramedX PracticeManagement systems (ww.intramedX.com) and InfoCure (ww.infocure.com).

[0089] In other variations, the physician could pay by the patient oralternatively according to a flat fee allowing use of the system for aset period of time (e.g. $100/month). In this case, the interface couldbe through a web page, eliminating the need for any extra equipment onthe part of the physician. In this way a trial version of the softwarecould even be downloaded to the physician's practice management system

[0090] (e.g., for x days free). Moreover, there is an additional servicefor physicians, which is described in co-pending patent entitled“Physician's Referral Network”. This service enables physicians to makereferrals to one another based essentially upon barter currency, whichis transacted in conjunction with the referrals. The present system maybe used to provide an additional screening function for the referralsmade via the present approach.

[0091] Internally, the system statistically analyzes thepreviously-described variables, using standard descriptive data miningtechniques to determine the degree of relevance of each associatedvariable in predicting the likelihood of further future litigation basedupon past behavior. The receptionist or physician may also enter datarelevant to the condition of the patient such as the general impressionof the patient's overall present state of health or (for the physicianexclusively), the patient's symptoms, complaints, likely diagnosis orpotential diagnosis (such as if the diagnosis is potentially associatedwith a severe condition) this information can, in turn, be used topredict the likely disorder(s) (which could even be broken down by thephysician as a probability value)and its severity; the likelihood ofcomplications from the disorder (essential precursor of litigation) aswell as (in many cases) the likely ultimate treatment protocol and itsassociated likelihood of complications (another essential precursor oflitigation) are thus factored into the system's calculations.

EXAMPLE

[0092] There are obviously a multitude of ways in which the predictivemodel could be developed. This example shows one of many possibleapproaches:

[0093] First, a large database of patients is scanned for definingexamples of “litigious” or “non-litigious” patients. In the first case,any patient linked with a criminal record of legal fraud, or whoinitiated two or more medical malpractice lawsuits that weresubsequently dismissed because of insufficient evidence, will beconsidered a very high litigious risk. In the second case, any patientwho has undergone major levels of medical care (e.g., over $50,000 orover 5 procedures in the last 10 years) without ever involving a doctorlegally will be considered a very low litigious risk.

[0094] A set of explanatory vectors is then prepared, containing allavailable data linked to the patients selected as being very high orvery low risks. For example, for each patient i we could define:

Xi={xi1, xi2, xi3, xi4}

[0095] Where

[0096] xi1=dummy variable (0/1) representing association with Lawyer A.

[0097] xi2=dummy variable (0/1) representing association with Lawyer B.

[0098] xi3=Income level.

[0099] xi4=Age.

[0100] And we could also define Yi, where

[0101] Yi=1 if patient is very litigious

[0102] Yi=0 if patient is very un-litigious

[0103] In this case, the model will be structured as a logit regression(a type of linear regression that, while fed with a range of data,returns an output value ranging between zero and one).

Prob(Y=1|Xi,B)=exp(B′X)/(1+exp(B′X))

[0104] Where B=beta, a vector of coefficients that is estimated on thepreviously-described data set. The model will therefore assign a higherprobability to Y=1 when B′X is large.

[0105] Suppose the resulting coefficients are as follows:

[0106] B={10, −10, 1}. This indicates that Lawyer A is not associatedwith either type of patient (indicating a fairly neutral lawyer),whereas Lawyer B is strongly associated with litigious patents.Moreover, a high income is linked with those patients less likely tosue, whereas age doesn't have much impact (although its small positivevalue indicates aged patients are mildly correlated with litigation).

[0107] Now, when operating, the system will operate in two stages. Afterpatient identifying information has been provided for patient Xj,

[0108] Stage 1: Rule-based filter: Does patient Xj fit into either thehighly litigious or highly non-litigious categories, as previouslydefined? If so, simply return a litigation probability of zero or one.

[0109] Stage 2: Statistical Model. Using the previously-calculated valuefor coefficient vector B, calculate exp(B′Xj)/(1+exp(B′Xj))—this will bea value ranging between zero and one, indicating the likelylitigiousness of the patient. Note that vector B is multiplied value byvalue into the patient's data vector, which allows all the differentfactors to be taken into consideration. Thus, even if the patient issomewhat aged, a high income and association with Lawyer A will push theoverall score down, indicating the patient is a low risk venture for thephysician.

[0110] The system could be further enhanced through the offering ofsupplemental medical malpractice insurance: if the physician uses thepresent service and does not accept patients who fall above a certainprobability value for litigation (verified by a secure agent associatedwith the physician's billing software), the insurance would cover anyclaims over and above those covered by standard malpractice insurancepolicy and the physician's CAT fund. In a variation, the present systemcould actually be used as a lower premium version of the CAT fund. Thepresent service could even be used as a reduced premium form of thephysician's basic medical malpractice insurance in which premiums arcset based upon the system's predicted litigation-based monetary risk tothe physician. It should be noted that the system incorporates thosevariables already used in standard medical malpractice actuarial models.Thus the present service could incorporate an extended version of theservice for those physicians who are interested in lower medicalmalpractice insurance rates, e.g., as part of a special policy for usersof the system who follow certain recommendation criteria. One novelbusiness model, in fact, could even involve the creation and developmentof a special new insurance company, which is developed entirely forphysicians who incorporate the use of the present system (in which case,it would likely be implemented as a proprietary system).

[0111] 5. Data Sources and Collection

[0112] Several important issues must be considered in the design of thepresent system. One of these relates to the means for collecting andupdating the data, which is provided to the system. It is important tofirst determine whether and where the desired data exists in digitizedform (or, if not, it may be necessary to access it and enter it into thesystem via manual means, (e.g., from court house records)). There are avariety of services available in which it is possible to access on-linedatabases (for a fee) which contain considerable personal informationabout individuals. Such databases particularly in aggregate may containa history of such individuals. Legal databases containing case historiesfor legal professionals may also provide a useful resource, as would anyavailable on-line county courthouse records, which happen to be storedin database format. A very important aspect of the above is given thepotentially variable heterogeneous data formation, it is important toenable each of the various heterogeneous database formats to be able tocommunicate with each other. This requires translation software, whichis specific to each type of heterogeneous database software. In manycases, the software itself must be further customized to each individualdatabase to the extent that it has certain uniquely definablecharacteristics

[0113] Sources of data might include:

[0114] a) Standard legal databases, with names of plaintiffs anddefendants involved in medical litigation.

[0115] b) Court transcripts, which would include such further details asthe names of expert witnesses. One potentially valuable data aggregationof this information is a commercial vendor called Knowledge X(ww.knowledgeX.com) which contains complete legal database informationas well).

[0116] c) On-line news sources, such as those provided by Nexis/Lexis.Natural language processing techniques could scan these databases ofnews stories for evidence of past medical litigation. Once a candidatestory is located, the names of the defendants and plaintiffs could besearched for in tandem, such that the eventual outcome of the case(settlement, trials, dismissal by the court, etc.) could be noted. Courtcases which involve the dismissal of a plaintiffs case would be ofspecial interest, as the plaintiff, lawyers, and professional witnessesinvolved would be suspect.

[0117] d) Medical board records, which would provide the names ofdoctors either being commended or condemned by other doctors undervarious circumstances.

[0118] e) Information from the National Data Bank to the extent that itis available for access by the present service. This should also includethe physician's entered response to the allegations of medicalmalpractice or practice restrictions which are recorded within the DataBank.

[0119] f) On-line and printed legal advertisements. The names of lawyersobserved being overly aggressive in their solicitation of malpracticecases could be recorded. In other words, certain lawyers would beflagged as “ambulance chasers”, and patients who are also clients ofthose lawyers (or likely to become clients, given their locale), wouldexperience an adverse impact on their score.

[0120] g) Insurance records. These would hold evidence of previouslawsuits, and would be useful for linking family groups.

[0121] h) Medical records.

[0122] i) Demographic and income databases.

[0123] j) Courthouse records.

[0124] Additional Potential Applications

[0125] 1. Incorporation into Patient Referral Forms

[0126] The information used in the present prescreening process canreadily be incorporated into the current mechanism widely used bymanaged care specialty referral forms. In this case the HealthMaintenance Organization (HMO) would implement the use of the presentsystem to screen patients being referred to specialists for specialtymedical services. The issuance of the patient referral form by the HMOwould then also be subject to medico-legal clearance via the abovesystem and this information would be entered directly on to the existingpatient referral form as an additional prerequisite for HMO approval ofthe referral.

[0127] It is worthy to note that this additional HMO screening ofpatients according to degree of litigiousness would put additionalpressure upon the referring physician to implement the present system,in order to insure that their patients who need quality specialty careare able to receive it subject to referral approval by the HMO. Thus, itis certainly conceivable in this scenario, that patients who are likelyto be very litigious, who are accordingly screened out by the HMO anddenied medico-legal clearance for referral are likely to need a higherpremium form of insurance provided either by the same insurer or by aseparate high risk specialty insurer (as described below). It is alsoworth noting that highly litigious patients are likely to becomeapparent to employers who offer insurance benefits through group plansto their employees inasmuch as they will typically not pass the initialapplication level screening by the HMO for that group plan policy.Moreover, in such cases employers may further consider employees who arehigh risk from a medico-legal litigiousness standpoint to also be highrisk for potential litigation against the present prospective employerwho may, in turn, consider not hiring that employee. Accordingly thispropensity on the part of employers could readily become a furtherdissuading factor for patients to sue physicians in the first place.

[0128] 2.High Risk Premium Patient Insurance—

[0129] It is entirely plausible to assume that HMOs would implement thepresent system to screen patients at all levels of HMO patient approval,i.e., at the time of application for enrollment, the applicant would, ofnecessity, have to be approved through the system as implemented by theinsurer. Both primary and secondary (or subsequent insurers) may wish toindependently implement the present system for purposes of assuring thatthe proper screening has occurred and because each insurer is likely tohave differing criteria for acceptance, rejection and associatedpremiums categories. In this way the actuarial formula of the insurermay incorporate additional attributes which are relevant to overallmedico-legal litigation risks instead of purely medical data alone,i.e., predicted patient litigiousness in addition to present and pastmedical conditions such as those attributes detailed within the presentinvention. In addition, the present improved actuarial model may also beused for patient insurance renewal in the same fashion as is used in thepatient application process. Unless regulatory agencies placerestrictions on which types of variables related to the patient (and towhat degree) these variables can be used in determining insurability andpremiums of the patient, the same revised actuarial model whichincorporates the attributes of the present invention in order todetermine over all litigation risk for purposes of insurability and ratesetting should also be used for HMO approved medico-legal clearancereferrals. Of course, rejection of the referral would have to besuperseded by a doctor's judgment if the case is determined to be amedical emergency. For patients who are considered “high litigationrisk” the insurer, instead of denying insurance coverage altogether, theinsurer may, at the application stage, or at the insurance renewalstage, in many cases place the patient in a higher risk category (forwhich there may be multiple high-risk categories). Or another insurerwho specializes in high-risk insurance may be available to providecoverage for those cases, which do not pass the acceptance criteria ofstandard HMOs. Thus, a higher premium form of insurance whether providedby a specialized carrier or as a higher risk category of the standardinsurer would have to be provided by the primary insurer and probably bythe secondary and tertiary insurer as well.

[0130] 3. Minimizing Medico-Legal Risk by Optimizing the Appropriatenessof the Match between the Physician and the Patient.

[0131] Although the primary goal in minimizing the chances ofmedico-legal litigation is to initially and preemptively screen out thehighest risk patients for litigation, there are additional furthermeasures that can be taken to additionally MINIMIZE the overallprobability of encountering ultimate medico-legal liability issues. Inparticular, it would be in the interest of hospitals and clinics to besure that once a patient has been appropriately screened for anunnecessarily high degree of litigiousness, to be sure that there isalso a good match between the patient and the physician based upon thespecific detailed initial complaints and symptoms (as well as medicalhistory) which together would be suggestive of the likely type ofdisorder or system involved which could be valuable data for purposes ofimproving and, in turn, optimizing selection of the physician(s) whobased upon their specific skill sets and the associated clinicallydemonstrated proficiency thereof would be most appropriately suited forthat particular patient. Accordingly, such an approach further ensuresthat physicians who are not optimally (or at a minimum not adequately)skilled and proficient with regards to certain system disorders, diseaseprocesses (or even diagnoses) which are likely to be associated with thepresent patient symptoms and medical history actually do not ultimatelytreat the patient (notwithstanding emergency or other potentialextenuating circumstances). Currently, the standard protocol by whichcertain physicians have rights to perform certain procedures is verycrude and is based upon each individual “delineation of hospitalprivileges” (or commonly known as “hospital privileges”). Within its ownparticular venue, each hospital has the inherent right to dictate whichparticular medical procedures and treatments (delineation of privileges)are performed and by whom. Typically, the chief of each department isassigned the responsibility of determining this delineation ofprivileges for each physician practicing at that hospital under his/herjurisdiction. However, this approach unlike the aforementioned which isherein proposed is often based largely upon subjective opinion and isoften even influenced heavily by politics which occur internal to thatspecific hospital. Moreover, in accordance with the presently acceptedprotocols, there is no consideration whatsoever given to the uniquephysical conditions and associated medical history of the patient orwhether the physician has specific medical knowledge or expertise whichmatches these medical profiles of the patient. There is thus asubstantial and unrecognized need in the attempt to further reducemedico-legal risk for a more sophisticated scheme which applies detailedknowledge of each patient including present condition(s) as well as pastmedical history and family history in combination with a detailedhistory of each physician's experience and the associated success andshortcomings related to this experience. Typically review of delineationof clinical privileges occurs only every two years on cursory review ofa department chief. There is currently little objective physicianvolume/success data available for review in granting clinicalprivileges. Data presently available is incomplete and, in most cases,no data is available nor is it requested at the time of the review andgranting of clinical privileges. Hospitals and regional medicalsocieties will have available internal data banks which will representan ongoing evaluation of all physicians and all disease processestreated in respect to staged severity of disease and in respect tosuccess/failure rate (which is relative to this determined stagedseverity of the disease) on a case-by case basis as well as categoryspecific, case type specific (predictive success/failure rate for anygiven newly introduced or developing case), and overall success/failurerates. Variations of the present statistical algorithm as abovedescribed will be implemented to calculate from this data the optimumpredicted conditions of physician and medical practice and/or medicalcenter for optimum treatment of each patient. It will include completemedical practice history of all physicians subscribing to the servicesuch as success/failure statistics, complete litigation history, etc.and other variables as described above. Particularly valuable attributesfor medical centers, hospitals and clinics may include the profiles ofthe physician who would be treating the patient (typically a specialistin referral cases), the profiles of the other physician(s) who would be(or would likely be) treating the patient (either for other specificmedical care or the likely attending physician), general quality ratingsor reputation of hospital support staff, medical testing and treatmentequipment and facilities which are relevant to the patient's medicalneeds and their associated quality and degree of overall importance tothe patient's present medical needs.

[0132] This statistical algorithm will also determine which point in theprogression of the medical status, as well as which point in thetreatment process is the most optimally appropriate circumstances torefer the patient to another physician or medical center, in as much asthe present statistical algorithm is able to consider both where anoptimally suitable physician for the present medical status of thepatient is located as well as consider where the most opportune medicalsupport staff is located, as well as other relevant attributes such asmore subjective aspects of this algorithm such as the appropriatenessand quality of the testing and treatment equipment available at thecenter as well as determine the quality of the staff overall. Regionaland personal financial interests and political considerations must beset aside in deference to objective optimum patient care. As a result ofthe predictive nature of the use of the present algorithm in a datamining application, somewhat more subjective data will be gleaned fromthe algorithm which will efficiently direct educational and trainingresources to determine which geographic and specialty areas to emphasizefor training programs by determining the relative distribution oftrained medical specialists in each specialty area. Ideally such analgorithm would incorporate longer term predictions based upon such dataas predicted demographic changes, anticipated technical advances in eachfield (determining the relative need for newly trained professionals) aswell as present staff admissions and areas of training emphasis of otherhospitals, clinics and teaching medical centers and the emphasis andprofiles of regional independent medical practitioners (which would beindicative of type and quality thus effective competition for referralsby the present system on a given locality basis).

[0133] The present system would be of considerable interest tohospitals, insurance companies, clinics, or private practitioners. Forexample, hospitals may use such a scheme as an improved model forapproving, denying or redirecting physical referrals to other doctors.It is of value to apply the same basic data model as described above inorder to accurately predict the associated risks of complications foreach patient (and with this data determine the medico-legal risks byalso considering the patient's degree of litigiousness) based upon thatphysician's history of clinical treatment to other patients who are mostsimilar to that of the present one. The statistical algorithm could, forexample, determine across a large data set of physicians and patientswhich key features of the physician are most predictive of success (thusultimately non-litigation) fort hat particular patient's medical status.

[0134] Physician data sources include those relevant ones to physicianquality and expertise such as physicians training and history of casesperformed (which historically is data submitted to the hospital by thephysician) including, of course, most relevantly how many of the sametypes of cases were seen and the percentage of those treated which weresuccessful cases. This data should also include the litigationstatistics. The information relating to the patient is available throughsimilar sources.

[0135] Typically such detailed patient data is available throughdigitized hospital records, insurance data bases, physician medicalrecords such as patient charts (including practice management databases)and other data detailed above in section 3

[0136] Some of these patient records would include present medicalstatus and conditions, medical history, medical history of familymembers and previous litigation history.

[0137] Patient data includes court transcripts and legal databases aswell as a variety of other data sources such as those described above insection 3. It is important to note that physician data not onlyincorporates attributes representing qualitative data indicating thetype of experience (degree of similarity of the experience to that ofthe patient's present medical status which is currently being presented)and quantitative data (number of previous cases seen which are of arelevant nature to the present one) but also the relative degree ofoverall success in treating the relevant patients seen and overallrelative degree of success for all patients previously treated overall,where relative degree of success may be a numeric percentage score ofhow the present physician's success compares historically as a ratio toother physicians on a given similar case by case basis which is, ofcourse, in turn, averaged overall for each physician. “Success” may bedetermined by such variables as nature and severity of complication andmorbidity as well as mortality rates and subjective assessment by thephysician during past treatment cases and follow-up visits. Medico-legalactivity may be another useful variable provided that these actualstatistical values are normalized by the predicted degree ofmedico-legal litigiousness of the patients which actually sue and inthis statistical model details of the nature of the medico-legalcomplaint are considered as well as the ultimate outcomes of the suits.Effectively, a matching score between the physician and the patient iscalculated as well as that of the other physicians who are alsopresently viable alternatives to the present physician. For purposes ofhospital clinic or physician specific implementation, a number of rulesfor example could be constructed automatically or manually based upondata analysis of overall success/failure rates for various types ofphysician/patient statistical correlation. For example, as aphysician/patient matching score (below X may not be suitable under anycircumstances notwithstanding emergency, etc.) Or, if another availablephysician presently is (or becomes) higher than the present physicianpresently treating the patient and this amount exceeds the score byamount Y (or amount Y if the present physician's score is at or abovenot unacceptably low (however, nonetheless sub-optimal) score withinrange Z, the patient could be instead referred onto another physicianwho is better or more specifically experienced with regards to thepatient's present medical needs. Geographic variables could also beincorporated into such rules as well as such factors as the degree ofthe matching score of the hospital staff (if relevant) to that of thepatient's needs.

[0138] Medico-legal pressures and insurance company pressures willrepresent the primary motivating factors which will compel the medicalproviders to adapt the presently described protocol. As a result, it isanticipated that one potential consequence of wide spread use of thepresent system is that very qualified physicians and particularlyqualified and focused specialists are likely to receive a large numberof patients via the present system. The same is true of very highquality medical centers such as those with a particular medical focusand emphasis. As such, it is likely that such a resulting quality baseddemand scenario, once it emerges within the healthcare field, will drivesuch high quality primary physicians, specialists, clinics and medicalcenters to not only preferentially select patients of low liability riskbut also those who are able and willing to pay independently for higherquality healthcare (in addition to or even independently of HMOcoverage). Those who are able to still justify some of their services tobe paid by medical insurance may offer certain routine services whilealso providing premium services for an additional fee which is chargedat a higher rate than insurance would cover (that is if it would evencover it in the first place). Moreover, it is likely in this scenariothat extremely high demand physicians, clinics and medical centers mayoffer services exclusively at a rate which requires additional fees tobe covered by the patient directly for the care of a surgeon,preferentially select those patients who appear to require complicated,unusual or lengthy surgical procedures as well as those who are willingto pay for non-HMO covered specialty treatments such as preventivetreatment and therapeutic regimens and also patients who choose to payfor non-HMO covered diagnostic tests involving advanced technology,technical skills and equipment. Because the types of treatments which aphysician offers patients affects litigation risk, the optimum pricewhich the physician should charge for each treatment is influenced byoverall demand of the patient population (more particularly the segmentof the potential patient population which the physician actuallyprovides that particular treatment for) as well as litigation risk ofthat patient population being treated. This value may be determined byan optimization technique which is designed for this type ofmulti-variable problem techniques are well known in the field ofstatistics.

[0139] In certain cases in which the decision as to the most appropriatetreatment regimen is not entirely clear cut or is of a somewhatsubjective nature, because certain risks or some complicationsassociated with each potential treatment regimen (as well as the risksassociated with resulting litigation) will tend to be different, thesystem may provide the physician with a comparative predicted estimateof the various risks associated with this potential for resultinglitigation for each relevant additional treatment regimen. Usingoptimization techniques, the present methodology may also be alsotailored to identify an optimal relative volume of different kinds ofpatients, based on the size of the pool of potential patient selectionavailable to the physician (which is a function of litigation riskprobability and probability/potential for monetary profit which are alsosubtractive variables). The optimization technique may also use datafrom numerous other physician's billing systems in order to predictivelysuggest this optimal volume distribution of different patient types (andultimately treatment types). In light of attempting to achieve anoptimal price (for optimal profitability) the physician may wish tocharge for these non-HMO covered (or patient supplemented) medicalservices in order to optimize for example, likelihood and degree ofprofitability or to optimize this value while also maintaining risk oflitigation of the type which could harm his/her practice within anacceptably low level such that long-term probability and degree ofprofitability is optimized. In an analogous application HMOs withinreasonable or regulatory limits may wish to set rates for certaintreatments based upon the same types of variables. It would even bepossible to adjust premiums based upon consideration of the variablesassociated with which patients are actually treated and which treatmentsare actually given to those patients. This approach would furtherincentivize the physician to choose to accept those types of patientswho are not only the most profitable in light of their overall low riskof litigation as well as those treatments which represent the lowestrisk of litigation and the highest returns from a profitabilitystandpoint. Because the profitability potential of certain treatments(and on perhaps certain types of patients) may represent a different (insome cases opposing) long term monetary value for the physician comparedwith that of the insurer, it may be in the insurer's best interest toadjust for this factor by setting the rates, e.g., by furtheraccentuating the cost of premiums for those treatments which are notonly higher risk but also higher profitability potential for thephysician.

[0140] At a more general level, the presently described scheme embodiesa profound paradigm shift which would indeed represent a much moreefficient commercial model for healthcare which is quality-market drivenlargely exists as the pro-quota for most other industries withincapitalist countries. Moreover, it is worthy to note that the mereintroduction of the present system will drive the further and ongoingdemand for its use in health care.

[0141] For the implementation within HMOs, the present physician/patientappropriateness score could be the most accurate model for determiningHMO based medico-legal clearance for patient referrals as described insub-section 1 within the present section (in as much as a very accuratedetermination of medical risk is factored into the overall medico-legalliability prediction scheme and finally the physician/patientappropriateness matching score is likely to be an extremely valuablemetric in malpractice actuarial models used by HMOs for use in theapproval of policy renewal procedures and as well as risk categoryallocation for the patient.

[0142] Similarly, this matching score between the patient and theprospective physician is an appropriate additional variable to be addedto the HMOs algorithm used to determine medico-legal clearance forreferral of the patient to a particular identified specialist, and ifthe matching criteria is inadequate or even sub-optimal, the presentsystem may recommend another local physician who is more suitable forthat particular patient (e.g., is on the same hospital staff, or hasassociated hospital privileges) or for non-hospital patients suchreferral recommendations by the insurer (or physician for physicianpractices or clinic) could be based upon locality including degreethereof using zip code information of the patient based implementations)compared to office or hospital locations at which each physicianpractices. It should be noted this particular approach would be idealfor a large scale automated referral system which is described inco-pending patent application entitled “Physicians Referral Network”, inas much as a very large pool of physicians with profiles of expertiseavailable via the network and at any given relevantly required physicallocality or hospital.

[0143] 4. Commercial Marketing—Certainly, there are useful applicationsof the present system to commercial marketing. Hospitals, HMOs,physicians, clinics, pharmacies and pharmaceutical companies spendbillions of dollars per year on consumer advertising. For example, areaswhich have a high degree of litigiousness based upon demographic datashould be weighed against the profit opportunity of those areasminimizing a market campaign by geographic area. In the case ofpharmaceutical companies in particular, litigation is a major problem,however, the litigation predictions generated by the data model would,of course, be for litigation against the drug maker and some slightlydifferent variable may be important compared to predicted litigationagainst health care providers (although not to minimize the potentialrelevance of litigation history, particularly against health careproviders). An example is, the likelihood and likely degree of severityof health risks and potential harm to the patient associated with adrug. This may include, of course, anecdotal evidence such as chemicaland biochemical similarities with the nature and physiological actionsof the drug (respectively) as well as (if available side effects andhealth problems associated with preliminary trials on humans and animalstudies as well as if the drug has already been released commercially)the documentation of medical side effects, complications and mortalityregarding their numbers and . . . all variables associated with rates ofoccurrence as well as (importantly) completed litigation history.Accordingly, targeted direct marketing via marketing database lists arealso an important form of advertising for each of the above commercialcategories. The present system would be usefully employed as a tool forscreening out those individuals and households, which are demonstratedor predicted to have litigious propensities.

[0144] The present invention for screening litigious clients iscertainly extensible into other paramedical and non-medical professionaldomains including but not limited to legal services, financial planningand advisory services, tax advisory services, stock brokers, investmentbrokers and dealers, and engineering firms. In these alternativeprofessional domains for which the present system may be adaptivelymodified, it would be obvious to the artfully skilled reader that thefeatures as applied to physicians for purposes of predicting futureprobability of litigation for a given service to a particular user canbe appropriately applied to analogously similar features which are,however, instead relevant to the specific professional domains of theparticular professional service provider (e.g., professionalcredentials, previous litigation for particular types of servicesrendered, etc.).

[0145] The present system could also be used by employers to screenpotential employees for litigious propensities. In this latter examplethe general inherent risks for monetary loss to the employer associatedwith ultimate litigation could be a useful variable within the datamodel, e.g., is the position associated with certain litigation pronerisks (such as occupational hazards) and if so, to what degree? Again,direct marketing initiatives within other professional services domains(as well as by the way potentially any/all direct marketing initiativeswherein the associated potential service to be rendered or product to besold carries with it the potential for certain recognized consumerliability risks) could benefit by implementing variations of the presentinvention as a screening tool (in which the variables used in thepredictive litigiousness risk model are adapted appropriately to theparticular domain to which it is applied). Again as in the medical case,the monetary risks associated with litigation could be weighed againstthe predicted monetary profits on a case by case basis.

1. We claim a method for constructing and implementing the use of a userprofile which is utilized for purposes of determining a statisticalpropensity of said user to engage in litigious behavior against apotential provider of services, products or other benefits.